We propose a novel probabilistic solver for PDE and inverse problems based on a recent generalization of Gaussian process named Q-exponential process. The method yields more accurate solutions and provides meaningful uncertainty quantification.

Whitened Score diffusion models enable stable training with arbitrary Gaussian noise by avoiding covariance inversion, improving performance on inverse problems with structured noise.